RADE
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RADE: Random Add-Drop Edge as a Regularizer
arXiv:2606.00757v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information. Stochastic graph augmentations (e.g., edge deletion) regularize training against overfitting but can introduce train-inference misalignment and do not improve over-squashing. In contrast, rewiring methods improve connectivity to mitigate over-squashing, but are not designed to regularize training.